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数字孪生技术作为一种将物理对象与其虚拟副本相结合的方法，已在智能制造、智慧城市、物联网（IoT）、大数据和人工智能（AI）等领域取得显著成果。本文旨在深入研究数字孪生技术在实际应用中所面临的挑战及其解决方法。首先，本文回顾了数字孪生的基本概念和发展历程，重点关注了其在仿真制造行业的应用，并分析了在产品全生命周期中的关键作用。接着，本文详细讨论了数字孪生技术目前所遇到的困境，如数据质量与精度、可视化与交互性提升、模型与知识分析存储、数学分析与仿真技术、模型快速构建技术、多领域应用实践以及实时性应用等方面的挑战。

为解决这些挑战，本研究从三个方面提出了高效性能、易用性、以及快速构建场景的通用数字孪生平台方案，并尝试进行实际应用。首先，在数据质量和精度方面，本文构建了一种自主分布式实时消息通信网络，以确保在硬件网络内满足消息实时性要求，并在整个系统内保证消息的发送、分发、存储和响应效率。其次，在模型与知识分析存储方面，本研究提出了一套基于图谱或语义构建逻辑模型的系统，该系统为用户提供方便的场景内各节点交互响应逻辑编辑以及虚拟场景内仿真逻辑编辑能力。通过参数化设计模型及模型间交互逻辑，并设计合适的模型存储结构，大大简化了模型与知识在各端的存储和传输。最后，在实时性应用方面，本研究基于前两个技术，探索了系统全生命周期应用方案技术，从历史数据分析、实时场景内数据监控与逻辑响应，到未来部分数据推演仿真，实现单个孪生场景的历史、现在与未来数据统一整合，大幅拓展了数字孪生技术的应用范围。

尽管如此，在实际应用场景中，我们仍需面临诸如海量信息的建模与处理、模型的结构化和专有化等复杂现实问题。在单一场景中，可能需要构建数千个数字孪生实体并处理每秒数十万级别的数据。如何利用现有知识快速推演模型，以及如何关联、分析和响应海量异构数据，依然是在探索研究前述两个技术方向时需要努力解决的现实难题。

总之，数字孪生技术在多领域实践中的应用和挑战仍有待深入研究。本文通过回顾数字孪生技术的发展历程、分析其在仿真制造行业的应用和挑战，以及提出针对这些挑战的部分解决方案，旨在为数字孪生技术的进一步研究和应用提供借鉴。随着数字孪生技术的不断发展，有望在智能制造、智慧城市等领域实现更高效、可持续和人性化的解决方案。


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\begin{eabstract}
As a method of combining physical objects with their virtual replicas, digital twin technology has achieved remarkable results in the fields of smart manufacturing, smart cities, Internet of Things (IoT), big data and artificial intelligence (AI). This paper aims to provide an in-depth study of the challenges and solutions of digital twin technology in practical applications. Firstly, this paper reviews the basic concept and development process of digital twin, focuses on its application in the simulation manufacturing industry, and analyzes the key role in the product life cycle. Then, the difficulties encountered by digital twin technology are discussed in detail, such as data quality and accuracy, visualization and interactivity improvement, model and knowledge analysis and storage, mathematical analysis and simulation technology, rapid model construction technology, multi-field application practice, and real-time application challenges.

In order to solve these challenges, this study proposes a universal digital twin platform scheme with efficient performance, ease of use, and rapid construction of scenarios from three aspects, and attempts to apply it to practical applications. Firstly, in terms of data quality and accuracy, this paper constructs an autonomous distributed real-time message communication network to ensure that the real-time requirements of messages are met within the hardware network, and the efficiency of message sending, distribution, storage and response is guaranteed throughout the system. Secondly, in terms of model and knowledge analysis and storage, this study proposes a system based on graph or semantic construction logic model, which provides users with convenient interactive response logic editing ability of each node in the scene and simulation logic editing ability in the virtual scene. By parameterizing the design of the model and the logic of interaction between the models, and designing the appropriate storage structure of the model, the storage and transmission of the model and knowledge at each end are greatly simplified. Finally, in terms of real-time application, based on the first two technologies, this study explores the whole life cycle application scheme technology of the system, from historical data analysis, data monitoring and logical response in real-time scenes, to partial data deduction and simulation in the future, realizing the unified integration of history, present and future data of a single twin scene, greatly expanding the application scope of digital twin technology.

However, in practical application scenarios, we still need to face complex practical problems, such as massive information modeling and processing, model structuring and proprietary. In a single scenario, it may be necessary to build thousands of digital twins and process hundreds of thousands of levels of data per second. How to use existing knowledge to quickly deduce models, and how to associate, analyze and respond to massive heterogeneous data are still practical problems that need to be solved when exploring the above two technical directions.

In conclusion, the applications and challenges of digital twin technology in multi-domain practice still need to be deeply studied. By reviewing the development process of digital twin technology, analyzing its applications and challenges in the simulation manufacturing industry, and proposing some solutions to these challenges, this paper aims to provide reference for the further research and application of digital twin technology. With the continuous development of digital twin technology, it is expected to realize more efficient, sustainable and humanized solutions in smart manufacturing, smart city and other fields.
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